Opioid-Induced Constipation: Cost Impact of Approved Medications in the Emergency Department
Bibliographic record
Abstract
INTRODUCTION: Opioid-induced constipation (OIC) prescription medications (OIC-Rx) like methylnaltrexone subcutaneous (SC) have shown efficacy in treating OIC in the emergency department (ED). This study aimed to describe and compare healthcare resource utilization (HRU) and healthcare costs in ED patients with OIC receiving OIC-Rx versus those not receiving OIC-Rx. METHODS: Adult patients with OIC during an ED encounter were identified from a hospital-based ED encounters database (2016-2019) and classified on the basis of receipt of OIC-Rx (OIC-Rx versus No OIC-Rx cohorts). Entropy balancing was used to reweight characteristics of the two cohorts. HRU and healthcare costs were measured and compared during the ED encounter and 30-day post-discharge period. RESULTS: Among 11,135 patients in the OIC-Rx cohort (21,474 in the No OIC-Rx cohort), 93% received methylnaltrexone SC. Patients in the OIC-Rx cohort had 0.7 fewer inpatient days per OIC ED encounter and 64% decreased odds of being hospitalized versus the No OIC-Rx cohort (both p < 0.001). During the post-discharge period, the OIC-Rx cohort had 35% decreased odds of any re-encounter (p < 0.001). The OIC-Rx cohort had a $732 reduction in costs per OIC ED encounter versus the No OIC-Rx cohort (p < 0.001), driven by larger hospitals and patients with Medicare or Commercial insurance. During the post-discharge period, the OIC-Rx cohort had a $421 reduction in costs associated with any re-encounter versus the No OIC-Rx cohort (p = 0.004). CONCLUSION: Patients receiving OIC-Rx in the ED had decreased odds of being hospitalized and fewer re-encounters in the 30-day post-discharge period versus patients who did not receive OIC-Rx, resulting in cost savings for insurance agencies and healthcare providers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".